Exploring and describing data

Data Fluency

Konstantinos Kattidis

Data Analytics Lead

Scenario: analyzing the customer engagement drop

Katia is a customer success manager at "Xcelerate Solutions"
She identified a drop in user engagement
She is now analyzing data to find the root cause of the issue:
- Checking for outliers and ensuring data quality
- Creating data visualizations

Woman with her laptop exploring data

She found that user engagement drops during periods of poor software performance
She confirms her findings by checking the customer feedback data
This example demonstrates:
- How the ability to explore and describe data leads to finding the root cause of the business problem

Woman showing graph with correlation

Doctor warning about data issues

Data exploration helps identify patterns, trends, outliers, and potential insights
Data fluent individuals can create data visualizations
Ranging from bar charts and line charts to histograms and box plots
They can recognize spikes, variations, or relationships within the data

Person exploring

Data-fluent individuals can effectively describe data
This helps to effectively draw conclusions from the data
It involves providing a summary of the dataset's characteristics:
- Measures of central tendency
- Measures of variability
- Measures to assess the relationship between variables

Person with a distribution graph

Person thinking about analytics results

Considering the context in which the data was collected
- Taking into account external factors that impact the data
Approach data analysis with a critical and curious mindset:
- Questioning assumptions
- Challenging interpretations
- Seeking alternative explanations

Data-fluent individuals have the skills to use the required data tools and technologies:
- Data visualization and reporting tools
- Spreadsheet software
Generative AI leads to new possibilities for data fluency!

Person working with tools and technologies

Data Fluency